The present invention relates to an apparatus and method for efficient representation of periodic and nearly periodic signals for analysis.
Analysis of periodic or nearly periodic signals does not, as a rule, rely solely on an inspection of single periods themselves. In many cases, inspection of an evolution of changes in a periodic part of a signal over time may reveal important information. However, in relatively long signals (containing a large amount of periods) a standard continuous representation of the signal makes inspection of the signal (i.e. the entire data) a tedious and ineffective task, especially if signal changes are gradual and moderate. One specific example of this problem is found in electrocardiogram (ECG) analysis of the human heart.
An ECG describes the electrical activity of the heart's muscle, which initiates and accompanies its mechanical activity. An ECG signal is recorded by body surface electrodes that measure the change in electrical potentials over the body due to the propagating electrical activation in the heart. Visual inspection of the ECG signal is the basic and most common non-invasive means for detection and diagnosis of cardiac abnormalities. The main features forming the basis for ECG diagnosis, which give indication of gross morphological changes, are: the P wave, describing the depolarization of the atria; the QRS complex, describing ventricles depolarization; and the T wave, describing ventricle repolarization.
Thus, extraction of information related to heart activity by means of ECG inspection and analysis concentrates on what is known as the P-QRS-T segment of the signal. However, analysis typically ignores large portions of the ECG signal—those portions corresponding to periods between any two consecutive heart beats. With the exception of the identification and interpretation of cardiac arrhythmias, most of the commonly used diagnostic aids based on ECG data, such as an S-T segment shift, prolonged and bizarre QRS complex patterns, or T wave inversion are—as indicated by their names—related primarily to inspection of the P-QRS-T segment of the signal.
The significant frequency range of ECG signals is traditionally considered to be from 0.05–100 Hz. Although all common diagnostic methods mentioned above are based solely on information contained in the 0.05–100 HZ frequency range, valuable information is known to be found in higher frequencies. Abboud et al (“High-Frequency Electrocardiogram Analysis of the Entire QRS in the Diagnosis and Assessment of Coronary Artery Disease”, Progress in Cardiovascular Diseases, Vol. XXXV, No. 5, March/April 1993), the contents of which are hereby incorporated by reference, have shown in a series of studies a correlation between significant decrease in the high frequency (HF), namely 150–250 Hz, content of the QRS signal and an ischemic condition of the heart.
In both traditional ECG based diagnosis methods and in more recent HF ECG based methods, it is common that cardiac abnormalities (ischemia being the most important) which are not present at rest, may be manifested during physiological stress, usually caused by exercise. Thus, comparison of an ECG signal of a subject under physiological stress with the same subject's ECG signals at rest and during a recovery period is commonly used for detection and identification of cardiac abnormalities. It should be noted, however, that existing continuous representations of the ECG signal do not allow easy inspection of the evolution of a signal during a test—the test being typically 10–20 minutes long, thus involving many hundreds of heart beats.
While standard ECG diagnosis methods may be (and actually are) based on local data, the situation in HF ECG methods is a more delicate one: as signal to noise ratio is far worse in the HF range than in the standard 0.05–100 HZ range, accuracy of local data in the HF portion of a signal might not be sufficient. Therefore, whereas a global representation of a standard ECG signal of a complete exercise test may be viewed as a diagnostic aid, serving as an improvement upon traditional ECG analysis methods, it is of utmost importance in the diagnosis and interpretation of HF ECG signals.
Further studies by Beker et al (“Analysis of High Frequency QRS Potential during Exercise Testing Patients with Coronary Artery Disease and in Healthy Subjects”, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, 1995), the contents of which are hereby incorporated by reference, showed that a decrease of the HF ECG of a QRS complex during exercise test could serve as an indication for early detection of ischemic pathologies. These findings make inspection of an HF ECG highly interesting, and call for development of tools that may tackle problems such an inspection imposes. An HF ECG signal has a significantly lower amplitude than a standard ECG, and therefore an HF ECG cannot be usefully dealt with, without first improving its signal to noise ratio.
The results presented by Beker et al. are based on a comparison of the HF ECG signal at rest with the same signal during exercise. However, the HF ECG signal is susceptible to drastic and sudden changes that are not necessarily due to an ischemic condition of the heart, but rather to outer changes such as the patient's body position. Any diagnostic tool based on HF ECG will have to differentiate between these sudden changes and the changes in the signal caused by ischemia.
It is clear from the discussion above that a local inspection of the HF ECG signal is prone to errors for the following reasons:                It seems impossible to reduce the level of noise to a completely insignificant one, without distorting the HF ECG signal itself. Thus some level of noise will always occur in the signal, and its overall effect should be evaluated over a relatively long timescale.        It seem impossible to determine, by inspecting the HF level of two different QRS complexes, whether the difference between these signals is due to ischemia or to an artifact caused by a movement of the patient.        